“Be Professional, Private and Pleasant”: The Conscious and Unconscious Gendering of Campaign Messages in Canadian and Australian Local Elections
Bibliographic record
Abstract
This dissertation examines Australian and Canadian local campaigns to investigate the extent to which gender and gendered stereotypes consciously affect candidates’ campaign messaging. The data for this study was gathered via in-depth interviews with 92 candidates who contested elections at the state/provincial level between 2010 and 2013. The data collected during the interviews included information on how candidates presented themselves in terms of their appearance, qualifications, character traits and family life; the issues that they highlighted in their local campaigns; the voters they targeted and strategies to connect with them; and information about their opponent relationships such as whether they formed civility pacts, employed negative attack messaging and how they responded if they were negatively campaigned against. The main conclusion is that gender affects political campaigns. Women’s campaign messaging looks different from men’s campaign messaging in several ways. For example, women are less likely to share personal information about themselves and their families and less likely to target an opponent with negative attack messages despite being more likely to be the target of such attacks. Among the most competitive women candidates, the differences found between their campaigns, and men’s campaigns, regardless of competitiveness, started to diminish. In terms of understanding why campaigns are gendered, there was minimal evidence detected that candidates consciously adjusted their messaging in response to what they perceived to be either voter-held or self-held beliefs about gendered stereotypes. Thus, gendered campaign messaging is the result of unconscious gender role stereotypes. By and large, women candidates did not cue gender in their local campaigns by highlighting women’s issues in their messaging, or by appealing to voters to support a woman candidate.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".